WICOX: Weight-Based Integrated Cox Model for Time-to-Event Data in Distributed Databases Without Data-Sharing

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 1; pp. 1 - 12
• Main Authors: Park, Ji A., Kim, Tae H., Kim, Jihoon, Park, Yu R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Bioinformatics; Biological system modeling; Biomedical data; Computer Communication Networks; Cox proportional hazard model; Data models; distributed algorithm; Distributed databases; Hazards; horizontally partitioned data; Hospitals; Humans; Information Dissemination - methods; Institutions; Intensive care; Iterative methods; Performance prediction; Predictive models; Privacy; privacy protection method; Proportional Hazards Models; Risk analysis; Risk factors; Robustness; Statistical inference; Survival; Time dependence
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2022.3218585

To exploit large-scale biomedical data, the application of common data models and the establishment of data networks are being actively carried out worldwide. However, due to the privacy issues, it is difficult to share data distributed among institutions. In this study, we developed and evaluated weight-based integrated Cox model (WICOX) as a privacy-protecting method without sharing patient-level information across institutions. WICOX generates a weight for each institutional model and builds an integrated model of multi-institutional data based on these weights. WICOX does not require iterative communication until the centralized parameter converges. We performed experiments to show the weight characteristic of our algorithm based on 10 hospitals (2910 intensive care unit (ICU) stays in total) from the electronic intensive care unit Collaborative Research Database to predict time to ICU mortality with eight risk factors. Compared with the centralized Cox model, WICOX showed biases from 0 to 0.68E-2, from 0.00E-2 to 4.98E-2, and from 0.74E-2 to 1.7E-2 for time-dependent AUC, log hazard ratio, and survival rate, respectively. In addition, through simulation results using real 10 hospitals, WICOX showed robust results in accuracy under any composition of hospitals. The results of the experiments highlight that WICOX has robust characteristics and provides predictive performance and statistical inference results nearly the same as those of the centralized model. WICOX is a non-iterative method using the weight of institutional model for implementing the Cox model across multiple institutions in a privacy-preserving manner.